A quantitative analysis of dynamic signal processing by transcription factors

转录因子动态信号处理的定量分析

• 批准号: 8750766
• 负责人: Nan Hao
• 金额: $ 33.92万
• 依托单位: UNIVERSITY OF CALIFORNIA, SAN DIEGO
• 依托单位国家: 美国
• 财政年份: 2014
• 资助国家: 美国
• 起止时间: 2014-08-15 至 2019-05-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8750766
• 关键词: Adopted; Behavior; Biological; Cell physiology; Cells; Computer Simulation; Cues; Cyclic AMP-Dependent Protein Kinases; Defect; Development; Disease; Eukaryota; Eukaryotic Cell; Event; Exhibits; Foundations; Gene Expression; Gene Expression Regulation; Gene Targeting; Genes; Genetic Transcription; Goals; Growth; Heart Diseases; Heterogeneity; Image Analysis; Kinetics; Lead; Malignant Neoplasms; Measurement; Microfluidic Analytical Techniques; Microfluidics; Modeling; Molecular; Monitor; Mutation; Organism; Pattern; Pharmacological Treatment; Phosphotransferases; Physiologic pulse; Population; Process; Processed Genes; Property; Protein Dynamics; Proteins; Regulation; Reporter Genes; Research; Research Personnel; Resistance; Role; Saccharomyces cerevisiae; Signal Pathway; Signal Transduction; Signaling Molecule; Stimulus; Stress; Structure; System; Testing; Time; Transcriptional Regulation; Translating; Variant; Work; Yeasts; base; cellular imaging; chromatin immunoprecipitation; computerized tools; data modeling; human disease; improved; interdisciplinary approach; mutant; novel therapeutics; programs; promoter; public health relevance; research study; response; signal processing; tool; transcription factor

Project Summary Cells respond to a wide range of environmental cues through intracellular signaling pathways. An increasing number of studies revealed that cells transmit environmental information by controlling the temporal dynamics of activities of signaling molecules. However, understanding how these dynamic patterns are decoded to influence cellular responses remains a challenging goal. Protein kinase A (PKA) is a highly conserved prototypic kinase that regulates many cellular behaviors, such as growth and stress resistance, through transcriptional programs. In response to environmental stimuli, PKA displays various dynamics of signaling activity. Defects in dynamic regulation of PKA activity can lead to disastrous diseases, such as cancer and heart disease. To understand the decoding mechanisms and functions of PKA dynamics, we recently developed a synthetic system in yeast S.cerevisiae that enables direct and precise control of intracellular signaling activities. This system is capable of revealing causal relationships and direct mechanistic connections between signaling dynamics and downstream responses, and hence is a useful tool for developing mechanistic models of signaling systems. In the proposed research, this dynamic control system will be integrated with single-cell imaging, high-throughput microfluidics and computational modeling to produce temporally controlled PKA inputs and to quantitatively investigate how these signaling dynamics are decoded to influence gene expression responses via a single transcription factor (TF) that is dynamically regulated, via two paralogous TFs with distinct dynamics, and via transcriptional network motifs. Modeling analysis suggests that target genes decode dynamics of TF input based on the kinetic properties of their promoters. In Aim 1, the kinetics of molecular processes that govern gene responses to TF dynamics will be determined and this information will be used to develop detailed kinetic models of transcription. These models will be further used and improved in Aim 2 to analyze how two seemingly redundant TFs can distinctly process signaling dynamics and how they contribute to the dynamic diversity of transcriptional responses. Single-cell imaging analysis will be used to test the model results. In Aim 3, we will build on the models from previous aims and investigate how network motifs, composed by multiple TFs and target genes, process and decode signaling dynamics. A high- throughput microfluidic platform will be used to track the abundance and subcellular localization of each motif components in single cells. Based on the single-cell data, modeling analysis will be conducted to analyze how distinct motifs diversify the dynamic responses to differentially decode temporal patterns of signaling inputs. The completion of this project will lead to a quantitative understanding about the decoding mechanisms and functional relevance of signaling dynamics and will lay the scientific foundation for computationally-guided pharmacological treatments of human diseases.

项目摘要 细胞通过细胞内信号通路对广泛的环境信号做出反应。越来越 许多研究表明，细胞通过控制时间动态来传递环境信息 信号分子的活动。然而，了解这些动态模式是如何解码的， 影响细胞反应仍然是一个具有挑战性的目标。蛋白激酶A（PKA）是一种高度保守的 一种原型激酶，通过以下途径调节许多细胞行为，如生长和应激抗性 转录程序。PKA在对环境刺激的反应中表现出不同的信号传导动力学 活动PKA活性动态调节的缺陷可导致灾难性的疾病，如癌症， 心脏病为了了解PKA动力学的解码机制和功能，我们最近 开发了一种在酵母酿酒酵母中的合成系统，该系统能够直接和精确地控制细胞内 信号活动。这个系统能够揭示因果关系和直接的机械联系 之间的信号动态和下游反应，因此是一个有用的工具，发展机制， 信号系统模型。在拟议的研究中，这种动态控制系统将与 单细胞成像，高通量微流体和计算建模，以产生时间控制的 PKA输入，并定量研究这些信号动力学是如何解码影响基因表达的。 表达反应通过一个单一的转录因子（TF）的动态调节，通过两个旁系同源 转录因子具有不同的动态，并通过转录网络基序。模型分析表明，目标 基因基于其启动子的动力学特性解码TF输入的动力学。在目标1中， 将确定控制基因对TF动力学反应的分子过程，这些信息将 用于开发详细的转录动力学模型。这些模型将在2010年得到进一步的应用和改进。 目的2分析两个看似冗余的TF如何能够不同地处理信号动力学，以及它们如何 有助于转录反应的动态多样性。单细胞成像分析将用于测试 模型的结果。在目标3中，我们将建立在以前目标的模型上，并研究网络如何 由多个TF和靶基因组成的基序处理和解码信号动态。一个高- 通量微流控平台将用于跟踪每个基序的丰度和亚细胞定位 单个细胞中的成分。基于单细胞数据，将进行建模分析，以分析 不同的基元使对信令输入的时间模式进行差分解码的动态响应多样化。 完成这个项目将导致对解码机制的定量理解， 信号动力学的功能相关性，并将为计算指导的科学基础 人类疾病的药物治疗。